W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.
W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.
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DOI：
10.3390/s21175801
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发表时间：
2021-08-28
期刊：
影响因子：
--
通讯作者：
Eckel SP
中科院分区：
文献类型：
--
作者：
Li K;Deng H;Morrison J;Habre R;Franklin M;Chiang YY;Sward K;Gilliland FD;Ambite JL;Eckel SP
时间序列分类的许多方法都依赖于机器学习方法。然而，人们越来越有兴趣超越黑箱预测模型，以了解时间序列的歧视性特征及其与结果的关联。一种很有前途的方法是时间序列小波（TSS），它可以识别时间序列的最大判别子序列。例如，在环境卫生应用中，TSS可用于确定与不良健康结果相关的暴露时间序列（shapelets）中的短期模式。TSS中候选小粒子的识别需要大量的计算。原始的TSS算法采用穷举搜索。随后的算法通过裁剪/聚合候选集或从初始值训练候选集来提高效率，但这些方法都有局限性。本文介绍了一种利用小波变换发现技术识别候选小波的智能方法——小波-TSS （W-TSS）。我们在两个数据集上测试了W-TSS:(1)以前TSS研究中使用的合成示例；(2)一个关于住宅空气污染传感器暴露与哮喘参与者症状之间关系的小组研究。与之前的TSS算法相比，W-TSS算法的计算效率更高，精度更高，并且能够发现更多的鉴别小波。W-TSS不需要预先指定小块长度。
Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.
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发表时间：
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期刊：
Bioinformatics (Oxford, England)
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影响因子：
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影响因子：
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